Denoising of EEG signal based on word imagination using ICA for artifact and noise removal on unspoken speech

Efy Yosrita, Rosida Nur Aziza, Rahma Farah Ningrum, Givary Muhammad


The purpose of this research is to observe  the effectiveness of Independent Component Analysis (ICA) method for denoising raw EEG signals based on word imagination, which will be used for word classification on unspoken speech. The electroencephalogram (EEG) signals are signals that represent the electrical activities of the human brain when someone is doing  activities, such as sleeping, thinking or other physical activities. EEG data based on the word imagination used for the research is accompanied by artifacts, that come from muscle movements, heartbeat, eye blink, voltage and so on. In previous studies, the ICA method has been widely used and effective for relieving physiological artifacts. Artifact to Signal Ratio (ASR) is used  to  measure the effectiveness of ICA in this study.  If the ratio is getting larger, the ICA method is  considered  effective for clearing noise and artifacts from the EEG  data.  Based on the experiment, the obtained ASR values from 11 subjects on 14  electrodes amounted are within the range of  0.910 to 1,080.  Thus,  it can be concluded that ICA is effective for removing artifacts from EEG signals  based on word imagination.


Denoising; EEG signal; ICA; Preprocessing

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